Subarea Partition Based on Correlation Analysis with Edge-Elimination Strategy Using Automatic License Plate Recognition Data

Abstract

To partition an urban network into several subareas (i.e., subarea partition) is a vital step for regional coordinated signal control. The correlation between intersections must be analyzed for achieving reliable subarea partition results. However, because of the incompleteness of spatial–temporal information in traffic data, previous studies merely explored the relationship between any intersections. Subarea partition considering the correlation of any pair of intersections remains a challenge in a large-scale network. This paper proposes a subarea partition method that integrates a novel correlation-degree model and the Newman fast algorithm with an edge-elimination strategy using automatic license plate recognition (ALPR) data. First, vehicle trips are extracted and a correlation-degree model is developed for measuring the relationship of any pair of intersections. Second, an edge-elimination strategy is proposed to generate candidate subarea partition solutions under conditions with different proportions of correlated edges. Finally, an optimal solution of subarea partition is identified by the ratios of ideal connected intersections to total intersections of different partition solutions’ correlation index (CI). The proposed method was implemented in a real-world urban network in Kunshan, China. The results show that the optimal partition solution can be obtained when the top 33% of correlated edges are maintained, and the ratio of ideal connected intersections’ CI is 72.35% with most of the intersections being connected, which demonstrates the rationality of the proposed partition method in large-scale urban networks.

A Study of Adjacent Intersection Correlation Based on Temporal Graph Attention Network

“…Bie et al [15] proposed the correlation degree index (CI) to represent the correlation degree of intersections by taking into account the cycle, flow, and road length and used numerical experiments; the calculation was complicated. Ke et al [16] proposed a correlation degree calculation method based on the distance and flow between adjacent intersections, combined with the Newman algorithm and an automatic license plate recognition algorithm, conducted subarea division experiments under large-scale road networks, and verified their effectiveness. Pang et al [17] proposed a coupling model of adjacent intersections of highways and established a density-based traffic transfer equation for evaluating the coupling strength of adjacent intersections.…”

“…On the one hand, as a classification model, compared with traditional machine learning models, the performance of TGAT is less restricted by input dimensions and has been shown to be applicable to multi-dimensional datasets such as Reddit, Wikipedia, Industrial, etc., and its classification accuracy is generally better than that of traditional machine learning models, so it can be used to widely select traffic features at intersections and more comprehensively describe the traffic status of intersections. On the other hand, since TGAT contains a self-attention mechanism, the self-attention value can be used as the node's relevance; compared with traditional relevance models [13][14][15][16][17], TGAT does not require the additional input of artificial parameters; and compared with static graphs such as GraphSage (Graph Sample and Aggregate), GAT, and so on, it can continuously adjust the weight coefficients of the neighbouring nodes in training, and it can more clearly show the influence of nodes on the current road network structure at different moments and obtain the dynamic correlation of intersections.…”